Single-cell co-expression analysis reveals that transcriptional modules are shared across cell types in the brain

Harris, Benjamin; Crow, Megan; Fischer, Stephan; Gillis, Jesse

doi:10.1016/j.cels.2021.04.010

Cited by 23 publications

(26 citation statements)

References 35 publications

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“…Marker genes and transcription factors known for cell type-specific expression also show significant covariation with cell types in other cell lineages (AUROC = 0.96 within meta-clusters and 0.98 within classes; Fig. 2I ), consistent with similar analyses of the BICCN mouse primary motor cortex data ( 38 ). We hypothesize that tightly coordinated, differential regulation of functionally conserved genes creates graded, continuous variations in expression levels across cell types, resulting in persistent cross-species coexpression at different scales of cellular organization ( 39 ).…”

Section: Resultssupporting

confidence: 81%

Conserved coexpression at single cell resolution across primate brains

Suresh

Crow²,

Jorstad

et al. 2022

Preprint

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Enhanced cognitive function in humans is hypothesized to result from cortical expansion and increased cellular diversity. However, the mechanisms that drive these phenotypic differences remain poorly understood, in part due to the lack of high-quality cellular resolution data in human and non-human primates. Here, we take advantage of single cell expression data from the middle temporal gyrus of five primates (human, chimp, gorilla, macaque and marmoset) to identify 57 homologous cell types and generate cell-type specific gene coexpression networks for comparative analysis. While ortholog expression patterns are generally well conserved, we find 24% of genes with extensive differences between human and non-human primates (3383/14,131), which are also associated with multiple brain disorders. To validate these observations, we perform a meta-analysis of coexpression networks across 19 animals, and find that a subset of these genes have deeply conserved coexpression across all non-human animals, and strongly divergent coexpression relationships in humans (139/3383, <1% of primate orthologs). Genes with human-specific cellular expression and coexpression networks (like NHEJ1, GTF2H2, C2 and BBS5) typically evolve under relaxed selective constraints and may drive rapid evolutionary change in brain function.

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Section: Resultssupporting

confidence: 81%

Conserved coexpression at single cell resolution across primate brains

Suresh

Crow²,

Jorstad

et al. 2022

Preprint

Self Cite

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show abstract

“…Single-cell analysis has multiple advantages over bulk analysis for addressing the molecular circuitry between gene (co-)expression and regulatory elements by (i) being able to reduce cell-type heterogeneity, or even study one specific cell type 17 , 18 , (ii) producing measurements per cell and thus getting closer in time to the transcription event and (iii) allow to detect co-expression events per individual (i.e., a single genetic background), and thus not affected by linkage disequilibrium (LD). Moreover, with the advent of multimodal single-cell datasets 19 , 20 , chromatin accessibility and gene expression levels can be measured in the same exact cells, which allows exploring the local regulatory elements affecting gene expression at a very high resolution.…”

Section: Introductionmentioning

confidence: 99%

Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis

2022

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Most human genes are co-expressed with a nearby gene. Previous studies have revealed this local gene co-expression to be widespread across chromosomes and across dozens of tissues. Yet, so far these studies used bulk RNA-seq, averaging gene expression measurements across millions of cells, thus being unclear if this co-expression stems from transcription events in single cells. Here, we leverage single cell datasets in >85 individuals to identify gene co-expression across cells, unbiased by cell-type heterogeneity and benefiting from the co-occurrence of transcription events in single cells. We discover >3800 co-expressed gene pairs in two human cell types, induced pluripotent stem cells (iPSCs) and lymphoblastoid cell lines (LCLs) and (i) compare single cell to bulk RNA-seq in identifying local gene co-expression, (ii) show that many co-expressed genes – but not the majority – are composed of functionally related genes and (iii) using proteomics data, provide evidence that their co-expression is maintained up to the protein level. Finally, using single cell RNA-sequencing (scRNA-seq) and single cell ATAC-sequencing (scATAC-seq) data for the same single cells, we identify gene-enhancer associations and reveal that >95% of co-expressed gene pairs share regulatory elements. These results elucidate the potential reasons for co-expression in single cell gene regulatory networks and warrant a deeper study of shared regulatory elements, in view of explaining disease comorbidity due to affecting several genes. Our in-depth view of local gene co-expression and regulatory element co-activity advances our understanding of the shared regulatory architecture between genes.

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“…With the advent of single-cell RNA sequencing (scRNA-seq), we have an unprecedented opportunity to reveal gene relationships in specific cellular contexts and to probe cellular-level networks (Trapnell 2015; Tanay and Regev 2017). One recent study used scRNA-seq from mouse brain samples to construct gene co-expression networks, comparing the topology of networks built from different levels of cell type hierarchy (i.e., from broad to specific classes) (Harris et al 2021). Their results show well-preserved gene-gene relationships at each level, and suggest the existence of a core co-regulatory network in the brain.…”

Section: Introductionmentioning

confidence: 99%

In search of a core cellular network with single cell transcriptome data in the fruit fly

Yang

Harrison

Promislow

2021

Preprint

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Background: Along with specialized functions, cells of multicellular organisms also perform essential functions common to most if not all cells. Whether diverse cells do this by using the same set of genes, interacting in a fixed coordinated fashion to execute essential functions, remains a central question in biology. Single-cell RNA-sequencing (scRNA-seq) measures gene expression of individual cells, enabling researchers to discover gene expression patterns that contribute to the diversity of cell functions. Current analyses focus primarily on identifying differentially expressed genes across cells. However, patterns of co-expression between genes are probably more indicative of biological processes than are the expression of individual genes. Using single cell transcriptome data from the fly brain, here we focus on gene co-expression to search for a core cellular network. Results: In this study, we constructed cell type-specific gene co-expression networks using single cell transcriptome data of brains from the fruit fly, Drosophila melanogaster. We detected a set of highly coordinated genes preserved across cell types in fly brains and defined this set as the core cellular network. This core is very small compared with cell type-specific gene co-expression networks and shows dense connectivity. Modules within this core are enriched for basic cellular functions, such as translation and ATP metabolic processes, and gene members of these modules have distinct evolutionary signatures. Conclusions: Overall, we demonstrated that a core cellular network exists in diverse cell types of fly brains and this core exhibits unique topological, structural, functional and evolutionary properties.

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Single-cell co-expression analysis reveals that transcriptional modules are shared across cell types in the brain

Cited by 23 publications

References 35 publications

Conserved coexpression at single cell resolution across primate brains

Conserved coexpression at single cell resolution across primate brains

Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis

In search of a core cellular network with single cell transcriptome data in the fruit fly

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